Well File:


Customs and Border Protection expands use of facial recognition


Customs and Border Protection is making progress testing and deploying their facial recognition technology at air, sea and land ports across the country. CBP is using the technology to scan travelers at 26 seaports and 159 land-ports and airports across the country, the Government Accountability Office told the House Committee on Homeland Security hearing on July 27. CBP installed facial recognition technology to biometrically confirm travelers' identities for all arriving and departing travelers.

'Degraded' Synthetic Faces Could Help Improve Facial Image Recognition


Researchers from Michigan State University have devised a way for synthetic faces to take a break from the deepfakes scene and do some good in the world – by helping image recognition systems to become more accurate. The new controllable face synthesis module (CFSM) they've devised is capable of regenerating faces in the style of real-world video surveillance footage, rather than relying on the uniformly higher-quality images used in popular open source datasets of celebrities, which do not reflect all the faults and shortcomings of genuine CCTV systems, such as facial blur, low resolution, and sensor noise – factors that can affect recognition accuracy. CFSM is not intended specifically to authentically simulate head poses, expressions, or all the other usual traits that are the objective of deepfake systems, but rather to generate a range of alternative views in the style of the target recognition system, using style transfer. The system is designed to mimic the style domain of the target system, and to adapt its output according to the resolution and range of'eccentricities' therein. The use-case includes legacy systems that are not likely to be upgraded due to cost, but which can currently contribute little to the new generation of facial recognition technologies, due to poor quality of output that may once have been leading-edge.

'The entire protein universe': AI predicts shape of nearly every known protein


The structure of the vitellogenin protein -- a precursor of egg yolk -- as predicted by the AlphaFold tool.Credit: DeepMind From today, determining the 3D shape of almost any protein known to science will be as simple as typing in a Google search. Researchers have used AlphaFold -- the revolutionary artificial-intelligence (AI) network -- to predict the structures of some 200 million proteins from 1 million species, covering nearly every known protein on the planet. The data dump will be freely available on a database set up by DeepMind, Google's London-based AI company that developed AlphaFold, and the European Molecular Biology Laboratory's European Bioinformatics Institute (EMBL-EBI), an intergovernmental organization near Cambridge, UK. "Essentially you can think of it covering the entire protein universe," DeepMind CEO Demis Hassabis, said at a press briefing. The 3D shape, or structure, of a protein is what determines its function in cells.

Humanoid diving robot explores shipwrecks on the bottom of the ocean

CNN Top Stories

Known as OceanOneK, the robot allows its operators to feel like they're underwater explorers, too. OceanOneK resembles a human diver from the front, with arms and hands and eyes that have 3D vision, capturing the underwater world in full color. The back of the robot has computers and eight multidirectional thrusters that help it carefully maneuver the sites of fragile sunken ships. OceanOneK, here doing an experiment in a swimming pool at Stanford University, resembles a human diver. When an operator at the ocean's surface uses controls to direct OceanOneK, the robot's haptic (touch-based) feedback system causes the person to feel the water's resistance as well as the contours of artifacts.

Artificial Intelligence in Dentistry: Past, Present, and Future


Artificial intelligence (AI) has remarkably increased its presence and significance in a wide range of sectors, including dentistry. It can mimic the intelligence of humans to undertake complex predictions and decision-making in the healthcare sector, particularly in endodontics. The models of AI, such as convolutional neural networks and/or artificial neural networks, have shown a variety of applications in endodontics, including studying the anatomy of the root canal system, forecasting the viability of stem cells of the dental pulp, measuring working lengths, pinpointing root fractures and periapical lesions and forecasting the success of retreatment procedures. Future applications of this technology were considered in relation to scheduling, patient care, drug-drug interactions, prognostic diagnosis, and robotic endodontic surgery. In endodontics, in terms of disease detection, evaluation, and prediction, AI has demonstrated accuracy and precision. AI can aid in the advancement of endodontic diagnosis and therapy, which can enhance endodontic treatment results. However, before incorporating AI models into routine clinical operations, it is still important to further certify the cost-effectiveness, dependability, and applicability of these models.

Could machine learning fuel a reproducibility crisis in science?


A CT scan of a tumor in human lungs. Researchers are experimenting with AI algorithms that can spot early signs of the disease.Credit: K. H. Fung/SPL From biomedicine to political sciences, researchers increasingly use machine learning as a tool to make predictions on the basis of patterns in their data. But the claims in many such studies are likely to be overblown, according to a pair of researchers at Princeton University in New Jersey. They want to sound an alarm about what they call a "brewing reproducibility crisis" in machine-learning-based sciences. Machine learning is being sold as a tool that researchers can learn in a few hours and use by themselves -- and many follow that advice, says Sayash Kapoor, a machine-learning researcher at Princeton.

Stick-on ultrasound patch hailed as revolution in medical imaging

The Guardian

A stick-on patch that can take an ultrasound scan of a person's insides as they go about their daily life has been hailed as a revolution in medical imaging. The wearable patch, which is the size of a postage stamp, can image blood vessels, the digestive system and internal organs for up to 48 hours, giving doctors a more detailed picture of a patient's health than the snapshots provided by routine scans. In laboratory tests, researchers used the patches to watch people's hearts change shape during exercise, their stomachs expand and shrink as they drank and passed drinks, and their muscles pick up microdamage when weightlifting. Prof Xuanhe Zhao at Massachusetts Institute of Technology, who led the research team, said the patches could "revolutionise" medical imaging because existing scans are very brief, sometimes lasting only seconds, and usually have to be performed in hospitals. Ultimately, Zhao envisions people buying boxes of the patches over the counter and using them, with help from smart algorithms on their mobile phones, to monitor their heart, lungs and digestive systems for early signs of disease or infection, or their muscles during rehabilitation or physical training.

AI's Potential to Tackle Crime in Europe


In the years to come, artificial intelligence will be a key feature of cross border criminal investigations, a joint report by Eurojust and eu-LISA, the union's official IT agency found. AI technologies can increase cooperation between EU member states in tackling crime, however, authorities must be careful since machine learning algorithms are prone to biases. AI was listed as a priority in the EU's e-Justice Action plan for 2019-2023. In a world where crime is borderless and criminals employ sophisticated communication tools and technologies, including encryption and AI; tackling crime requires cross-border cooperation by EU Member States and the application of technologies on par with those used by the criminal groups, urged Friday's report. "The field of justice is undergoing digital transformation, and artificial intelligence, as a set of different technologies, has great potential to contribute to and further enhance this process, allowing for a significant improvement in both the efficiency and effectiveness of operation of the judicial authorities," the report said.

Novel Machine Learning Technique May Help ID Tremor Specific to MS...


A new machine learning strategy was able to differentiate tremor specific to multiple sclerosis (MS) from tremor related to other diseases, with more than 90% accuracy, according to a new study. "The proposed method, with high classification accuracy and strong correlations of [tremor] features to clinical outcomes, has clearly demonstrated the potential to complement the existing tremor-diagnostic approach in MS patients," the researchers wrote. The study, "Machine learning aided classification of tremor in multiple sclerosis," was published in eBioMedicine. Tremor, or uncontrolled shaking, can occur as a symptom of MS, usually associated with damage to part of the brain called the cerebellum. In fact, according to the researchers, about half of MS patients experience this symptom.

A day in the life of a Chinese robotaxi driver

MIT Technology Review

Robotaxi safety operator is an occupation that only exists in our time, the result of an evolving technology that's advanced enough to get rid of a driver--most of the time, and in controlled environments-- but not good enough to convince authorities that they can do away with human intervention altogether. Today, self-driving companies from the US, Europe, and China are racing to bring the technology to commercial application. Most of them, including Apollo, the self-driving arm of Baidu, have started on-demand robotaxi trials on public roads yet still need to operate with various constraints. With an associate degree in human resources, Liu has no academic training related to this job, But he has always loved driving, and he acted as the driver for his boss in a previous role. When he heard about the self-driving technologies, his curiosity pushed him to look up related jobs online and apply.